Fig. 1: Overview of the workflow for immunophenotyping, proteogenomic, functional, and validation analyses for neoantigen identification in the cross-entity cohort.

Tumor material and peripheral blood from 32 patients included in the ImmoNEO MASTER cohort harboring diverse tumor entities was used for the following analyses: a Tumor microenvironment phenotyping; single cell suspensions from fresh primary tumor tissues were used for multi-color flow cytometric characterization of tumor-infiltrating T cells and FACS-sorted CD8⁺ T cells were used for bulk transcriptome analysis (RNA-seq). b Genomic and transcriptomic analysis; primary tumor tissue was used for whole exome (WES)/whole-genome sequencing (WGS) and RNA-seq. Blood from the same patient served as control samples. Variants were called by MuTect2 (v4.1.0.0) from WES/WGS data and by Strelka2 (v2.9.10) from RNA-seq data and variants were filtered for single-nucleotide polymorphisms (SNPs) by using the dbSNP database. c Immunopeptidome analysis; fresh primary tumor tissue was used for HLA class I-bound peptide immunoprecipitation and subsequent liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis of eluted peptides. The whole HLA class I peptidome was analysed using pFind searching for 8–15mers. d MS-based neoantigen identification; patient-specific variant data from (b) were used to generate a personalized database for matching with the MS-identified peptide sequences using pFind for the identification of neoantigen candidates. The machine learning tool Prosit was integrated in addition to rescoring the peptide spectra matching to the patient-specific personalized database. Several filtering and post-processing steps were applied for the identification of neoantigen candidates. e Immunogenicity assessment of neoantigen candidates; patient-derived autologous immune cells (PBMCs and TILs) and allogenic-matched healthy donor-derived PBMCs were used for immunogenicity assessment of the identified neoantigen candidates using a modified accelerated co-cultured dendritic cell (acDC) assay. f In-depth validation of peptides and variants; identified peptides were verified by comparison of their spectra to their synthetic peptide spectra and Prosit-predicted spectra as well as comparing their experimental and predicted retention times. RNA variants were further validated for their tumor-specificity by analysing their prevalence in normal tissue RNA-seq data obtained from the Genotype-Tissue Expression (GTEx) project³⁵. APC antigen-presenting cell, FDR false discovery rate, HLA-I human leukocyte antigen class I, ORF open reading frame, m/z mass/charge number of ions, PBMC peripheral blood mononuclear cells, TIL tumor-infiltrating lymphocytes.